Instructions to use aoxo/flux.1dev-abliteratedv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aoxo/flux.1dev-abliteratedv2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("aoxo/flux.1dev-abliteratedv2", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Create README.md
Browse files
README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- black-forest-labs/FLUX.1-dev
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new_version: black-forest-labs/FLUX.1-dev
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pipeline_tag: text-to-image
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library_name: diffusers
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tags:
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- art
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---
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### Model Card for FLUX.1 [dev] Abliterated
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#### Model Overview
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**Model Name:** FLUX.1 [dev] Abliterated-v2
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**Model Type:** Text-to-Image Generation
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**Architecture:** Rectified Flow Transformer
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**Parameter Size:** 12 Billion
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**Base Model:** FLUX.1 [dev]
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**Modification:** Abliteration via Unlearning (Removal of Refusal Mechanism)
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#### Description
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The **FLUX.1 [dev] Abliterated-v2** model is a modified version of FLUX.1 [dev] and a sueccessor to FLUX.1 [dev] Abliterated. This version has undergone a process called **unlearning**, which removes the model's built-in refusal mechanism. This allows the model to respond to a wider range of prompts, including those that the original model might have deemed inappropriate or harmful.
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The abliteration process involves identifying and isolating the specific components of the model responsible for refusal behavior and then modifying or ablating those components. This results in a model that is more flexible and responsive, while still maintaining the core capabilities of the original FLUX.1 [dev] model.
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#### Architecture
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#### Usage
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To use the FLUX.1 [dev] Abliterated model, you can load it via Hugging Face and generate images using the following code:
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```python
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import torch
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from diffusers import AutoPipelineForText2Image
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# Load the abliterated model
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pipeline = AutoPipelineForText2Image.from_pretrained(
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"aoxo/flux.1dev-abliteratedv2",
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torch_dtype=torch.float16,
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token='your_hf_token'
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).to('cuda')
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# Generate an image from a text prompt
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prompt = 'A girl in bikinis sipping on a margarita'
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image = pipeline(prompt).images[0]
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# Display the image
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image.show()
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```
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#### Training Data
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The FLUX.1 [dev] Abliteratedv2 model is based on the original FLUX.1 [dev] model and the original Abliterated. The training data includes a wide range of visual and textual content, ensuring that the model can generate images for a variety of prompts.
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#### License
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The FLUX.1 [dev] Abliteratedv2 model is released under the same **FLUX.1 [dev] Non-Commercial License** as the original model. This license allows for personal, scientific, and commercial use, with certain restrictions. Please review the license terms before using the model in your projects.
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#### Citation
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If you use the FLUX.1 [dev] Abliteratedv2 model in your research or projects, please cite the original FLUX.1 [dev] model and the abliteration process as described in the blog post by Aloshdenny.
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```bibtex
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@misc{flux1dev,
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author = {Flux Team},
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title = {FLUX.1 [dev]: A 12 Billion Parameter Rectified Flow Transformer},
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year = {2023},
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howpublished = {\url{https://huggingface.co/aoxo/flux.1dev}},
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}
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@misc{flux1dev-abliterated,
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author = {aoxo},
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title = {Unlearning Flux.1 Dev: Abliteration-v2},
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year = {2025},
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howpublished = {\url{https://medium.com/@aloshdenny/unlearning-flux-1-dev-abliteration-v2-52af88ed60b5}},
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}
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```
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#### Contact
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For questions, feedback, or collaboration opportunities, please contact the Flux Team at [contact@flux.ai](mailto:contact@flux.ai).
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---
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